Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko, David A. Egger

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

6 Zitate (Scopus)

Abstract

Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning (ML) method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at a reduced computational cost. A linear-response model is used as a first step, and symmetry-adapted ML is employed for the higher-order contributions as a second step. We investigate the performance of the approach for several systems, including molecules and extended solids. The method can reduce the training-set sizes required for accurate dielectric properties and Raman spectra in comparison to a single-step ML approach.

OriginalspracheEnglisch
Seiten (von - bis)6464-6470
Seitenumfang7
FachzeitschriftJournal of Physical Chemistry C
Jahrgang128
Ausgabenummer15
DOIs
PublikationsstatusVeröffentlicht - 18 Apr. 2024

Fingerprint

Untersuchen Sie die Forschungsthemen von „Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren